The results of many clinical tests are quantitative and are provided on a continuous scale. To help decide the presence or absence of disease, a cut-off point for 'normal' or 'abnormal' is chosen. The sensitivity and specificity of a test vary according to the level that is chosen as the cut-off point. The receiver operating characteristic (ROC) curve, a graphical technique for describing and comparing the accuracy of diagnostic tests, is obtained by plotting the sensitivity of a test on the y axis against 1-specificity on the x axis. Two methods commonly used to establish the optimal cut-off point include the point on the ROC curve closest to (0, 1) and the Youden index. The area under the ROC curve provides a measure of the overall performance of a diagnostic test. In this paper, the author explains how the ROC curve can be used to select optimal cut-off points for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of tests. Conclusion:The ROC curve is obtained by calculating the sensitivity and specificity of a test at every possible cut-off point, and plotting sensitivity against 1-specificity. The curve may be used to select optimal cut-off values for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of different tests.

The usefulness of diagnostic tests,

Objectives: To assess whether vitamin D supplementation in infancy reduces the risk of type 1 diabetes in later life. Methods: This was a systematic review and metaanalysis using Medline, Embase, Cinahl, Cochrane Central Register of Controlled Trials and reference lists of retrieved articles. The main outcome measure was development of type 1 diabetes. Controlled trials and observational studies that had assessed the effect of vitamin D supplementation on risk of developing type 1 diabetes were included in the analysis. Results: Five observational studies (four case-control studies and one cohort study) met the inclusion criteria; no randomised controlled trials were found. Meta-analysis of data from the case-control studies showed that the risk of type 1 diabetes was significantly reduced in infants who were supplemented with vitamin D compared to those who were not supplemented (pooled odds ratio 0.71, 95% CI 0.60 to 0.84). The result of the cohort study was in agreement with that of the meta-analysis. There was also some evidence of a dose-response effect, with those using higher amounts of vitamin D being at lower risk of developing type 1 diabetes. Finally, there was a suggestion that the timing of supplementation might also be important for the subsequent development of type 1 diabetes. Conclusion: Vitamin D supplementation in early childhood may offer protection against the development of type 1 diabetes. The evidence for this is based on observational studies. Adequately powered, randomised controlled trials with long periods of follow-up are needed to establish causality and the best formulation, dose, duration and period of supplementation.Type 1 diabetes is characterised by autoimmune destruction of insulin-producing b cells in the pancreas. The specific factors that initiate the autoimmune process are not yet well understood, but b cell destruction often begins during infancy and continues over many months or years.

Health care professionals are increasingly required to base clinical decisions on the best available evidence. Evidence based medicine (EBM) is a systematic approach to clinical problem solving which allows the integration of the best available research evidence with clinical expertise and patient values. This paper explains the concept of EBM and introduces the five step EBM model: formulation of answerable clinical questions; searching for evidence; critical appraisal; applicability of evidence; evaluation of performance. Subsequent articles will focus on the principles and critical appraisal of randomised controlled trials, systematic reviews, and meta-analyses, and provide a practical demonstration of the five step EBM model using a real life clinical scenario.

Likelihood ratios summarize information about a diagnostic test by combining sensitivity and specificity. The Fagan's nomogram is a useful and convenient graphical tool that allows likelihood ratios to be used in conjunction with a patient's pre-test probability of disease to estimate the post-test probability of disease.